04-ggplot2

Author

Professor Shannon Ellis

Published

January 24, 2023

Data Visualization with ggplot2 ❤️ 🐧

Q&A

Q:Can we have a demo in class on how to clone our repo and check if our Github submissions submitted properly? I am having trouble doing this.
A: Yes! Let’s do that today.

Q: Does left_join(a, b) = right_join(b, a)?
A: No. The total information would be equivalent but the order of columns would differ. In the left join, the columns from a would be first and in the right join, the colums from b would be listed first. But the actual rows that are joined/included would be the same.

Q: I would still like to know if there is a way to save data sets more often when using functions on them, such as filter. I see we only saved data sets when we use functions like mutate, but why not other functions?
A: You’re allowed to store variables into a new data frame whenever you’d like! Now, the more variables you create, the more to keep track of, so I would encourage you to store your output any time you make a “substantial” change to your data. So, say you do a filter, selection, and mutate to get your data into the format for analysis. I wouldn’t store after each step, but I would store it after I wrote the pipe doing all three. Follow up if this doesn’t clarify!

Q: I think the left_join, right_join, and full_join was pretty confusing.
A: You’re right! It is confusing. We’ll go through more examples and get more practice!

Course Announcements

Due Dates:

  • Lab 03 due Friday (1/27; 11:59 PM)
  • Lecture Participation survey “due” after class

Course Announcements:

  • Happy New Year!
  • GitHub assignment clone + submission demo
  • Lab02 Notes:
    • Look at the answer key:
    • grouping by name and year
    • can add text outside of code chunks; discuss variable reference
    • solutions to optional exercises

ggplot2 \(\in\) tidyverse

  • ggplot2 is tidyverse’s data visualization package
  • Structure of the code for plots can be summarized as
ggplot(data = [dataset], 
       mapping = aes(x = [x-variable], 
                     y = [y-variable])) +
   geom_xxx() +
   other options

Data: Palmer Penguins

Measurements for penguin species, island in Palmer Archipelago, size (flipper length, body mass, bill dimensions), and sex.

# install.packages("palmerpenguins")
library(palmerpenguins)
glimpse(penguins)
Rows: 344
Columns: 8
$ species           <fct> Adelie, Adelie, Adelie, Adelie, Adelie, Adelie, Adel…
$ island            <fct> Torgersen, Torgersen, Torgersen, Torgersen, Torgerse…
$ bill_length_mm    <dbl> 39.1, 39.5, 40.3, NA, 36.7, 39.3, 38.9, 39.2, 34.1, …
$ bill_depth_mm     <dbl> 18.7, 17.4, 18.0, NA, 19.3, 20.6, 17.8, 19.6, 18.1, …
$ flipper_length_mm <int> 181, 186, 195, NA, 193, 190, 181, 195, 193, 190, 186…
$ body_mass_g       <int> 3750, 3800, 3250, NA, 3450, 3650, 3625, 4675, 3475, …
$ sex               <fct> male, female, female, NA, female, male, female, male…
$ year              <int> 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007, 2007…

Artwork by @allison_horst

The Data

penguins |>
  datatable()

A Plot

ggplot(data = penguins, 
       mapping = aes(x = bill_depth_mm, y = bill_length_mm,
                     color = species)) +
  geom_point() +
  labs(title = "Bill depth and length",
       subtitle = "Dimensions for Adelie, Chinstrap, and Gentoo Penguins",
       x = "Bill depth (mm)", y = "Bill length (mm)",
       color = "Species") +
  scale_color_viridis_d()
Warning: Removed 2 rows containing missing values (`geom_point()`).

Coding out loud

Start with the penguins data frame

ggplot(data = penguins)

Start with the penguins data frame, map bill depth to the x-axis

ggplot(data = penguins,
       mapping = aes(x = bill_depth_mm))

Start with the penguins data frame, map bill depth to the x-axis and map bill length to the y-axis.

ggplot(data = penguins,
       mapping = aes(x = bill_depth_mm,
                     y = bill_length_mm))

Start with the penguins data frame, map bill depth to the x-axis and map bill length to the y-axis. Represent each observation with a point

ggplot(data = penguins,
       mapping = aes(x = bill_depth_mm,
                     y = bill_length_mm)) + 
  geom_point()

Start with the penguins data frame, map bill depth to the x-axis and map bill length to the y-axis. Represent each observation with a point and map species to the color of each point.

ggplot(data = penguins,
       mapping = aes(x = bill_depth_mm,
                     y = bill_length_mm,
                     color = species)) + 
  geom_point()

Start with the penguins data frame, map bill depth to the x-axis and map bill length to the y-axis. Represent each observation with a point and map species to the color of each point. Title the plot “Bill depth and length”

ggplot(data = penguins,
       mapping = aes(x = bill_depth_mm,
                     y = bill_length_mm,
                     color = species)) +
  geom_point() +
  labs(title = "Bill depth and length")

Start with the penguins data frame, map bill depth to the x-axis and map bill length to the y-axis. Represent each observation with a point and map species to the color of each point. Title the plot “Bill depth and length”, add the subtitle “Dimensions for Adelie, Chinstrap, and Gentoo Penguins”

ggplot(data = penguins,
       mapping = aes(x = bill_depth_mm,
                     y = bill_length_mm,
                     color = species)) +
  geom_point() +
  labs(title = "Bill depth and length",
       subtitle = "Dimensions for Adelie, Chinstrap, and Gentoo Penguins")

Start with the penguins data frame, map bill depth to the x-axis and map bill length to the y-axis. Represent each observation with a point and map species to the color of each point. Title the plot “Bill depth and length”, add the subtitle “Dimensions for Adelie, Chinstrap, and Gentoo Penguins”, label the x and y axes as “Bill depth (mm)” and “Bill length (mm)”, respectively

ggplot(data = penguins,
       mapping = aes(x = bill_depth_mm,
                     y = bill_length_mm,
                     color = species)) +
  geom_point() +
  labs(title = "Bill depth and length",
       subtitle = "Dimensions for Adelie, Chinstrap, and Gentoo Penguins",
       x = "Bill depth (mm)", y = "Bill length (mm)")

Start with the penguins data frame, map bill depth to the x-axis and map bill length to the y-axis. Represent each observation with a point and map species to the color of each point. Title the plot “Bill depth and length”, add the subtitle “Dimensions for Adelie, Chinstrap, and Gentoo Penguins”, label the x and y axes as “Bill depth (mm)” and “Bill length (mm)”, respectively, label the legend “Species”

ggplot(data = penguins,
       mapping = aes(x = bill_depth_mm,
                     y = bill_length_mm,
                     color = species)) +
  geom_point() +
  labs(title = "Bill depth and length",
       subtitle = "Dimensions for Adelie, Chinstrap, and Gentoo Penguins",
       x = "Bill depth (mm)", y = "Bill length (mm)",
       color = "Species")

Start with the penguins data frame, map bill depth to the x-axis and map bill length to the y-axis. Represent each observation with a point and map species to the color of each point. Title the plot “Bill depth and length”, add the subtitle “Dimensions for Adelie, Chinstrap, and Gentoo Penguins”, label the x and y axes as “Bill depth (mm)” and “Bill length (mm)”, respectively, label the legend “Species”, and add a caption for the data source.

ggplot(data = penguins,
       mapping = aes(x = bill_depth_mm,
                     y = bill_length_mm,
                     color = species)) +
  geom_point() +
  labs(title = "Bill depth and length",
       subtitle = "Dimensions for Adelie, Chinstrap, and Gentoo Penguins",
       x = "Bill depth (mm)", y = "Bill length (mm)",
       color = "Species",
       caption = "Source: Palmer Station LTER / palmerpenguins package")

Start with the penguins data frame, map bill depth to the x-axis and map bill length to the y-axis. Represent each observation with a point and map species to the color of each point. Title the plot “Bill depth and length”, add the subtitle “Dimensions for Adelie, Chinstrap, and Gentoo Penguins”, label the x and y axes as “Bill depth (mm)” and “Bill length (mm)”, respectively, label the legend “Species”, and add a caption for the data source. Finally, use a discrete color scale that is designed to be perceived by viewers with common forms of color blindness.

ggplot(data = penguins,
       mapping = aes(x = bill_depth_mm,
                     y = bill_length_mm,
                     color = species)) +
  geom_point() +
  labs(title = "Bill depth and length",
       subtitle = "Dimensions for Adelie, Chinstrap, and Gentoo Penguins",
       x = "Bill depth (mm)", y = "Bill length (mm)",
       color = "Species",
       caption = "Source: Palmer Station LTER / palmerpenguins package") +
  scale_color_viridis_d()

Coding out loud

ggplot(data = penguins,
       mapping = aes(x = bill_depth_mm,
                     y = bill_length_mm,
                     color = species)) +
  geom_point() +
  labs(title = "Bill depth and length",
       subtitle = "Dimensions for Adelie, Chinstrap, and Gentoo Penguins",
       x = "Bill depth (mm)", y = "Bill length (mm)",
       color = "Species",
       caption = "Source: Palmer Station LTER / palmerpenguins package") +
  scale_color_viridis_d()
Warning: Removed 2 rows containing missing values (`geom_point()`).

Start with the penguins data frame, map bill depth to the x-axis and map bill length to the y-axis.

Represent each observation with a point and map species to the color of each point.

Title the plot “Bill depth and length”, add the subtitle “Dimensions for Adelie, Chinstrap, and Gentoo Penguins”, label the x and y axes as “Bill depth (mm)” and “Bill length (mm)”, respectively, label the legend “Species”, and add a caption for the data source.

Finally, use a discrete color scale that is designed to be perceived by viewers with common forms of color blindness.

Argument names

Tip

You can omit the names of first two arguments when building plots with ggplot().

ggplot(data = penguins, 
       mapping = aes(x = bill_depth_mm,  
                     y = bill_length_mm,
                     color = species)) +
  geom_point() +
  scale_color_viridis_d()
ggplot(penguins, 
       aes(x = bill_depth_mm, 
           y = bill_depth_mm,
           color = species)) +
  geom_point() +
  scale_color_viridis_d()

Your Turn

Generate a basic plot in ggplot2 using different variables than those in the last example (last example: bill_depth_mm & bill_depth_mm).

Put a green sticky on the front of your computer when you’re done. Put a pink if you want help/have a question.

Aesthetics

Aesthetics options

Commonly used characteristics of plotting characters that can be mapped to a specific variable in the data are

  • color
  • shape
  • size
  • alpha (transparency)

Color

ggplot(penguins,
       aes(x = bill_depth_mm, 
           y = bill_length_mm,
           color = species)) + 
  geom_point() +
  scale_color_viridis_d()

Shape

Mapped to a different variable than color

ggplot(penguins,
       aes(x = bill_depth_mm, 
           y = bill_length_mm,
           color = species,
           shape = island)) + 
  geom_point() +
  scale_color_viridis_d()

Shape

Mapped to same variable as color

ggplot(penguins,
       aes(x = bill_depth_mm, 
           y = bill_length_mm,
           color = species,
           shape = species)) + 
  geom_point() +
  scale_color_viridis_d()

Size

ggplot(penguins,
       aes(x = bill_depth_mm, 
           y = bill_length_mm,
           color = species,
           shape = species,
           size = body_mass_g)) + 
  geom_point() +
  scale_color_viridis_d()

Alpha

ggplot(penguins,
       aes(x = bill_depth_mm, 
           y = bill_length_mm,
           color = species,
           shape = species,
           size = body_mass_g,
           alpha = flipper_length_mm)) + 
  geom_point() +
  scale_color_viridis_d()

Mapping vs. setting

  • Mapping: Determine the size, alpha, etc. of points based on the values of a variable in the data
    • goes into aes()
  • Setting: Determine the size, alpha, etc. of points not based on the values of a variable in the data
    • goes into geom_*() (this was geom_point() in the previous example, but we’ll learn about other geoms soon!)

Mapping vs. Setting (example)

Mapping

ggplot(penguins,
       aes(x = bill_depth_mm,
           y = bill_length_mm,
           size = body_mass_g, 
           alpha = flipper_length_mm)) + 
  geom_point()

Setting

ggplot(penguins,
       aes(x = bill_depth_mm,
           y = bill_length_mm)) + 
  geom_point(size = 2, alpha = 0.5)

Your Turn

Edit the basic plot you created earlier to change something about its aesthetics.

Put a green sticky on the front of your computer when you’re done. Put a pink if you want help/have a question.

Faceting

Faceting

  • Smaller plots that display different subsets of the data
  • Useful for exploring conditional relationships and large data

ggplot(penguins, aes(x = bill_depth_mm, y = bill_length_mm)) + 
  geom_point() +
  facet_grid(species ~ island) 
Warning: Removed 2 rows containing missing values (`geom_point()`).

Various ways to facet

🧠 In the next few slides describe what each plot displays. Think about how the code relates to the output.

Warning

The plots in the next few slides do not have proper titles, axis labels, etc. because we want you to figure out what’s happening in the plots. But you should always label your plots!

ggplot(penguins, aes(x = bill_depth_mm, y = bill_length_mm)) + 
  geom_point() +
  facet_grid(species ~ sex) 

ggplot(penguins, aes(x = bill_depth_mm, y = bill_length_mm)) + 
  geom_point() +
  facet_grid(sex ~ species) 

ggplot(penguins, aes(x = bill_depth_mm, y = bill_length_mm)) + 
  geom_point() +
  facet_wrap(~ species) 

ggplot(penguins, aes(x = bill_depth_mm, y = bill_length_mm)) + 
  geom_point() +
  facet_grid(. ~ species) 

ggplot(penguins, aes(x = bill_depth_mm, y = bill_length_mm)) + 
  geom_point() +
  facet_wrap(~ species, ncol = 2)

Faceting summary

  • facet_grid():
    • 2d grid
    • rows ~ cols
    • use . for no split
  • facet_wrap(): 1d ribbon wrapped according to number of rows and columns specified or available plotting area

Facet and color

ggplot(
  penguins, 
  aes(x = bill_depth_mm, 
      y = bill_length_mm, 
      color = species)) + 
  geom_point() +
  facet_grid(species ~ sex) +
  scale_color_viridis_d()

Face and color, no legend

ggplot(
  penguins, 
  aes(x = bill_depth_mm, 
      y = bill_length_mm, 
      color = species)) +
  geom_point() +
  facet_grid(species ~ sex) +
  scale_color_viridis_d() +
  guides(color = FALSE)

geoms

Common geoms

geom 1 Description 2
geom_point scatterplot
geom_bar barplot
geom_line line plot
geom_density densityplot
geom_histogram histogram
geom_boxplot boxplot

Your Turn

Generate a plot in ggplot2 using a different geom than what you did previously. Customize as much as you can before time is “up.”

Put a green sticky on the front of your computer when you’re done. Put a pink if you want help/have a question.

Recap

  • Can I explain the overall structure of a call to generate a plot in ggplot2?
  • Can I describe ggplot2 code? Can I create plots using ggplot2?
  • Can I alter the aesthetics of a basic plot? (color, shape, size, transparency)
  • Am I able to facet a plot to generate a grid of figures
  • Can I describe what a geom is and do I know the basic plots available?

Suggested Reading

Footnotes

  1. ggplot2 geoms listed here↩︎

  2. When each visualization is appropriate here↩︎